{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、Titanic练习   特征分析与选择\n",
    "\n",
    "训练集中乘客的特征有：PassengerId，Pclass，Name，Sex，Age，SibSp，Parch，Ticket，Fare，Cabin和Embarked。\n",
    "\n",
    "接下来我们就对几个特征（变量）进行数据分析和可视化分析。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports pandas, numpy, matplotlib, seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "\n",
    "# pandas\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "# numpy, matplotlib, seaborn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的代码框里首先用pandas把数据读进来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### 删除没有用的变量 ['PassengerId','Name','Ticket']。删除之后可以用.info()再检查是否已删除这些字段。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500   NaN        S\n",
       "1         1       1  female  38.0      1      0  71.2833   C85        C\n",
       "2         1       3  female  26.0      0      0   7.9250   NaN        S\n",
       "3         1       1  female  35.0      1      0  53.1000  C123        S\n",
       "4         0       3    male  35.0      0      0   8.0500   NaN        S"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "del df['PassengerId']\n",
    "del df['Name']\n",
    "del df['Ticket']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embarked\n",
    "\n",
    "接下来研究下Embarked这个变量数据，Port of Embarkation，登船城市，C Q S 分别代表不同的城市。\n",
    "处理确失值，并且画出存活率与Embarked之间的折线图，统计频率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\zzh\\lib\\site-packages\\seaborn\\categorical.py:3717: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "D:\\Anaconda3\\envs\\zzh\\lib\\site-packages\\seaborn\\categorical.py:3723: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "D:\\Anaconda3\\envs\\zzh\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x25abcf86048>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Embarked\"] = df[\"Embarked\"].fillna(\"S\")\n",
    "\n",
    "# plot\n",
    "# 画出存活率和Embarked之间的折线图，统计频率\n",
    "sns.factorplot('Embarked','Survived', data=df,size=4,aspect=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来画一个有3个子图的柱状图：\n",
    "1. 将Embarked统计频次\n",
    "2. 按照是否survived将3种Embarked统计频次\n",
    "3. 在survived里统计三种Embarked的频率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25abd89f748>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axis1,axis2,axis3) = plt.subplots(1,3,figsize=(15,5))\n",
    "\n",
    "\n",
    "# 1. 将Embarked统计频次\n",
    "sns.countplot(x='Embarked', data=df, ax=axis1)\n",
    "\n",
    "# 2. 按照是否survived将3种Embarked统计频次\n",
    "sns.countplot(x='Survived', hue=\"Embarked\", data=df, order=[1,0], ax=axis2)\n",
    "\n",
    "# 3. 在survived里统计三种Embarked的频率\n",
    "# group by embarked, and get the mean for survived passengers for each value in Embarked\n",
    "embark_perc = df[[\"Embarked\", \"Survived\"]].groupby(['Embarked'],as_index=False).mean()\n",
    "\n",
    "sns.barplot(x='Embarked', y='Survived', data=embark_perc,order=['S','C','Q'],ax=axis3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Age\n",
    "\n",
    "接下来研究下 Age 这个变量数据。首先画一个年纪的分布图，再对年龄做数据处理，重新画图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x25abddc5488>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 911x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# .... continue with plot Age column\n",
    "\n",
    "# 继续画一张曲线图，按照survived分开画\n",
    "# peaks for survived/not survived passengers by their age\n",
    "facet = sns.FacetGrid(df, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, df['Age'].max()))\n",
    "facet.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25abd942ec8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据年龄统计存活的平均人数\n",
    "# average survived passengers by age\n",
    "fig, axis1 = plt.subplots(1,1,figsize=(18,4))\n",
    "average_age = df[[\"Age\", \"Survived\"]].groupby(['Age'],as_index=False).mean()\n",
    "sns.barplot(x='Age', y='Survived', data=average_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sex\n",
    "\n",
    "接下来研究下 Sex 这个变量数据，看看存活率跟性别有多大的关系。首先按照是否满16岁，将人群分为是否为\"成人\"，再分男人和女人。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画两张图：\n",
    "1. 按性别、成人统计人数\n",
    "2. 按性别和成人与否统计存活率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25abe1e0108>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_person(passenger):\n",
    "    age,sex = passenger\n",
    "    return 'child' if age < 16 else sex\n",
    "  \n",
    "df['Person'] = df[['Age','Sex']].apply(get_person,axis=1)\n",
    "# create dummy variables for Person column, & drop Male as it has the lowest average of survived passengers\n",
    "person_dummies_titanic  = pd.get_dummies(df['Person'])\n",
    "person_dummies_titanic.columns = ['Child','Female','Male']\n",
    "person_dummies_titanic.drop(['Male'], axis=1, inplace=True)\n",
    "\n",
    "df = df.join(person_dummies_titanic)\n",
    "\n",
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(10,5))\n",
    "\n",
    "# sns.factorplot('Person',data=titanic_df,kind='count',ax=axis1)\n",
    "sns.countplot(x='Person', data=df, ax=axis1)\n",
    "\n",
    "# average of survived for each Person(male, female, or child)\n",
    "person_perc = df[[\"Person\", \"Survived\"]].groupby(['Person'],as_index=False).mean()\n",
    "sns.barplot(x='Person', y='Survived', data=person_perc, ax=axis2, order=['male','female','child'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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